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Are you a Junior Machine Learning Engineer eager to turn messy, complex data into real-world intelligence?
We’re looking for a curious and motivated Junior ML Engineer to join a hybrid-working team building cutting-edge data intelligence tools for the financial sector. You’ll spend part of your time collaborating in person with engineers and data scientists, and part working remotely — giving you the best of both worlds.
You’ll be working on a platform that transforms unstructured private market data into actionable insights — learning how to design ML pipelines, fine-tune NLP models, and deploy solutions that really work in production.
In this role, you’ll help train, test, and optimise models that can read, understand, and structure complex documents at scale. From data preprocessing to model evaluation, you’ll gain hands-on experience across the machine learning lifecycle — while contributing to a product used by real-world clients.
What’s in it for you?
? AI That Matters – Work on models that make sense of unstructured financial documents and turn them into structured insights.
Hands-On ML Experience – Learn the full ML workflow — from cleaning data to deploying models and monitoring them in production.
? Mentorship & Growth – Work closely with experienced ML engineers who will guide your technical and career development.
? Collaborative Environment – Pair with engineers, data scientists, and domain experts to solve real-world challenges.
? Hybrid Flexibility – Balance focused remote work with in-person collaboration at our office.
What We’re Looking For:
- 0–2 years of experience in machine learning, applied AI, or data science (personal projects and internships count!)
- Solid Python skills and familiarity with libraries like PyTorch, TensorFlow, or Hugging Face Transformers
- Understanding of basic ML concepts and data preprocessing techniques
- Interest in NLP, unstructured data, and information extraction
- Eagerness to learn, take feedback, and contribute to a collaborative team
Nice to Have:
- Experience with SQL/NoSQL databases
- Familiarity with MLOps tools (Docker, Git, CI/CD)
- Exposure to vector databases or semantic search
- Knowledge of financial datasets or document processing workflows
If you’re excited to grow your skills in machine learning and work on technology that helps people understand complex data — we’d love to hear from you.
Apply now and start your journey building the future of data-driven insight.
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